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32nd International Conference on software engineering and Knowledge engineering, SEKE 2020

作者： Yang, Yi Mao, Xinjun Xu, Zixi Lu, Yao College of Computer National University of Defense Technology Changsha China College of Information Science and Engineering Hunan Womens' University Changsha China Key Laboratory of Complex Systems Software Engineering National University of Defense Technology Changsha China

ISBN: (纸本)1891706500

In community question answering (CQA) sites like Stack Overflow (SO), users provide contributions driven by community incentives and self-motivation, which gives rise to micro activities of individual user and macro emergence of community. According to our empirical research, the emergence of answer distribution in SO is of concern, about 90% questions have no more than two answers and almost 40% of questions have no accepted answer due to lack of alternative acceptable answers. In this paper, we explore CQA user contributions by considering both the external incentives of community and internal motivations of users, and study why CQA users contribute and how they influence the macro emergence of answer distribution. We present CQA community model based on normative multi-agent system approach, in which the users are modeled as agents and the community incentives as norms. The internal motivations are studied based on self-determination theory. The paper further analyzes how the internal and external factors together influence the activities of agents and ultimately the answer distribution of the whole community. We conduct experiments based on a simulation system and the SO dataset to validate the effectiveness of our proposed model. The results show that our model can reproduce the emergence that well matches up with the observation on real community. © 2020 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Multi agent systems

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Complementary multi-branch CNNs towards real-world 3D point classification

Complementary multi-branch CNNs towards real-world 3D point ...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Tang, Zifeng Hao, Fusheng Zhang, Qieshi Cheng, Jun Zhang, Jin Yuan, Shuai Shenzhen Institute of Advanced Technology Chinese Academy of Science CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems China Chinese University of Hong Kong Hong Kong Shenzhen University College of Computer Science and Software Engineering China Guangdong University of Technology College of Mechanical and Electrical Engineering China

ISBN: (纸本)9781665436786

Autonomous driving requires precise and efficient point clouds processing techniques, where deep learning has shown great potential. Nonetheless, most of the existing works achieve high accuracy in synthetic data while performing unsatisfactorily in real-world datasets. On the other hand, though existing multi-view-based, volumetric-based, and point-based methods have achieved promising results, they still suffer from high computational complexity or low memory utilization efficiency. For example, the computational time and memory consumption of multi-view-based and volumetric-based methods increase at most cubically with the increasing of the input resolution, while half of the point-based methods's runtime is wasted due to their irregular memory access. To address these issues, we propose a novel method to fuse complementary CNNs, dubbed as Multi-Branch Convolutional Neural Networks (MBCNN), which achieves the current state-of-the-art performance while maintaining high efficiency. The underlying design of MBCNN is to utilize the point-based convolution as a local-feature branch and spherical convolution as a global-feature branch, to capture patterns in space and exploit the merit of the pointwise MLP, which is robust to the perturbation. Besides, we design an efficient voxel-based indexing technique to prevent the feature-extracting phase from being blocked, thus significantly accelerating the procedure of finding a point's neighbor. Experimental results show that MBCNN achieves state-of-the-art performance on the real-world 3D point clouds dataset while reducing 30% of runtime in neighbor indexing. © 2021 IEEE.

关键词： Convolutional neural networks

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Boosting component-based synthesis with control structure recommendation 1

Boosting component-based synthesis with control structure re...

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1st ACM SIGSOFT International Workshop on Representation Learning for software engineering and Program Languages, RL+SE and PL 2020, co-located with ESEC/FSE 2020

作者： Liu, Binbin Dong, Wei Zhang, Yating Wang, Daiyan Liu, Jiaxin College of Computer Science National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9781450381253

Component-based synthesis is an important research field in program synthesis. API-based synthesis is a subfield of component-based synthesis, the component library of which are Java APIs. Unlike existing work in API-based synthesis that can only generate loop-free programs constituted by APIs, state-of-The-Art work FrAngel can generate programs with control structures. However, for the generation of control structures, it samples different types of control structures all at random. Given the information about the desired method (such as method name and input/output types), experienced programmers can have an initial thought about the possible control structures that could be used in implementing the desired method. The knowledge about control structures in the method can be learned from high-quality projects. In this paper, we propose a novel approach of recommending control structures for API-based synthesis based on deep learning. A neural network that can jointly embed the natural language description, method name, and input/output types into high-dimensional vectors to predict the possible control structures of the desired method is proposed. We integrate the prediction model into the synthesizer to improve the efficiency of synthesis. We train our model on a codebase of high-quality Java projects from GitHub. The prediction results of the neural network are fed to the API-based synthesizer to guide the sampling process of control structures. The experimental results on 40 programming tasks show that our approach can effectively improve the efficiency of synthesis. © 2020 ACM.

关键词： Efficiency

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Simultaneously Calibration of Multi Hand-Eye Robot System Based on Graph

arXiv

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arXiv 2023年

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi The Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Electrical Engineering and Computer Science Australian National University Canberra2601 Australia The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the relative pose between each unit, but the serialized incremental calibration strategy cannot avoid the problem of error accumulation in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method. © 2023, CC BY-SA.

关键词： Multipurpose robots

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Performance Analysis of Distributed Filtering under Misspecified Noise Covariances

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IEEE Transactions on Automatic Control 2025年

作者： Lyu, Xiaoxu Wen, Guanghui Shi, Ling Duan, Peihu Duan, Zhisheng The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Southeast University School of Automation Nanjing210096 China The Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Beijing Institute of Technology State Key Laboratory of CNS/ATM Beijing100081 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China

This paper systematically investigates the performance of the consensus-based distributed filter under misspecified noise covariances. First, we introduce four quantities: the nominal filter parameter, the nominal estimation error covariance, the ideal filter parameter, and the ideal estimation error covariance. We derive the difference expressions among these quantities and establish the corresponding one-step relations. These relations reveal how performance deteriorates when noise covariances are misspecified, and demonstrate how to evaluate the estimation error covariance using the available nominal filter parameter. We particularly highlight the effect of the information fusion step number on these relations. Furthermore, recursive relations are introduced by extending the results of the one-step relations. Subsequently, we demonstrate the convergence of these quantities under the collective observability condition, and show that the convergence condition of the nominal filter parameter can guarantee the convergence of the estimation error covariance. Additionally, we provide bounds on the estimation error covariance under misspecified noise covariances by utilizing the Frobenius norms of the noise covariance deviations and the trace of the nominal filter parameter. Finally, the effectiveness of the theoretical results is verified through numerical simulations. © 1963-2012 IEEE.

关键词： consensus analysis convergence analysis Distributed filtering misspecified noise covariance performance analysis

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A parallel framework for scene understanding 1

A parallel framework for scene understanding

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Zhang, Wenwen Wang, Jiangong Wang, Kunfeng School of Software Engineering Xi'An Jiaotong University Xi'an710049 China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9781665433372

Different related computer vision tasks make it possible to understand and analyze the scene totally. However, there is no useful framework combining different tasks for scene understanding. In this paper, we investigate the current state of scene understanding from different aspects relying on different computer vision tasks and propose an effective framework based on the theory of parallel vision to combine different tasks for scene understanding. With the maintained artificial scene throughout time from the proposed framework, we can understand the scene in an elegant way. To explain our proposed framework, a case study is given. © 2021 IEEE.

关键词： computer vision

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Multi-segment Bidding Behaviors of Power Units: Price-capacity-pair Analysis and Modeling 36

Multi-segment Bidding Behaviors of Power Units: Price-capaci...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Hou, Shuangshuang Wang, Ying Li, Lili Zhao, Jing Ning, Jia Zhang, Kaifeng Southeast University College of Software Suzhou China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China State Grid Electric Power Research Institute Nari Group Corporation Nanjing China Nanjing Institute of Technology School of Electric Power Engineering Nanjing China

ISBN: (纸本)9781665437127

With the deregulation of the power market, power units are allowed to make strategic biddings. The bidding results of the strategic bidding market are substantially different from the ones in the previous regulated market. Such differences in the bidding results will further have impacts on the power flow and system security. Thus, it is important to analyze and establish a model to simulate the bidding behaviors of the power units. This paper proposes a price-capacity-pairs model to simulate multi-segment bidding behavior. We select five vital factors that affect biddings behaviors as input: load, capacity, supply-demand ratio, bidders' bidding tendency and unit type. Based on the three-segment bidding rule, the price-capacity pair is used. For uncertain price or capacity, we choose a truncated normal distribution model. Scenario generation and reduction are achieved by Monte Carlo simulation and fuzzy C-means clustering algorithm. We apply our method to a case study based on Guangdong's market in China. The simulation of wind power units, coal power units and gas power units has verified the correctness of the model. © 2021 IEEE.

关键词： Power markets

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Exploiting crowd-based learning method in software engineering course 15

Exploiting crowd-based learning method in software engineeri...

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15th International Conference on computer Science and Education, ICCSE 2020

作者： Mao, Xinjun Lu, Yao Yang, Yi National University of Defense Technology Key Lab. of Software Engineering for Complex Systems Changsha China National University of Defense Technology College of Computer Changsha China

ISBN: (纸本)9781728172675

software engineering is one of main major course for computer science. The main objective of this course is to improve the engineering capabilities of students to develop complex software systems. However, current course practice methods are teacher-centered and thus highly depend on the limited talents, experiences and efforts of teachers. Such methods greatly restrict students' scopes to gain necessary skills and artifacts, and to draw on successful experiences to address various personalized engineering issues and therefore enhance abilities to tackle engineering complexity. This paper presents a crowd-based learning method and introduces our exploration to use the method to reform the software engineering course practice. The essence of our exploration is to exploit the wisdoms and artifacts of crowds over Internet to support students to accomplish their course projects. The former is the engineering knowledges shared in question and answering (QA) platforms such as StackOverflow, and the latter is the open source software (OSS) hosted in OSS sites such as GitHub. We use the method in two separate course practices that aim to improve students' abilities to develop original, high-quality and large-scale software systems. The results of three-year explorations are positive and impressive and show that the crowd-based learning method is effective. © 2020 IEEE.

关键词： Open source software

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Deficient Excitation in Parameter Learning

arXiv

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arXiv 2025年

作者： Cao, Ganghui Wang, Shimin Guay, Martin Wang, Jinzhi Duan, Zhisheng Polycarpou, Marios M. State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Massachusetts Institute of Technology CambridgeMA02139 United States Queen’s University KingstonONK7L 3N6 Canada KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This paper investigates parameter learning problems under deficient excitation (DE). The DE condition is a rank-deficient, and therefore, a more general evolution of the well-known persistent excitation condition. Under the DE condition, a proposed online algorithm is able to calculate the identifiable and non-identifiable subspaces, and finally give an optimal parameter estimate in the sense of least squares. In particular, the learning error within the identifiable subspace exponentially converges to zero in the noise-free case, even without persistent excitation. The DE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary DE conditions. This protocol allows each local estimator to operate locally in its identifiable subspace, and reach a consensus with neighbours in its non-identifiable subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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